Artificial Intelligence in Personalised Education with the Development of Critical Thinking

Artificial Intelligence in Personalised Education with the Development of Critical Thinking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial Intelligence in Personalised Education with the Development of Critical Thinking Dr. Nandini Banerjee, Susmita Rakshit, Dr. Madan Singh Deupa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8969990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Artificial Intelligence (AI) has transformed modern-day education by facilitating customized learning experiences, but there are still issues about how it affects the growth of critical thinking skills. This experiment touches upon the issue of the tradeoff between algorithm-based personalization and the development of more advanced cognitive functions of AI-assisted learning. The study sets out to investigate the strengths, weaknesses, opportunities and threats of AI in personalised education, define pedagogical and ethical issues and give strategic suggestions of how AI can be used with reflective and analytical pedagogy. Based on a qualitative SWOT analysis model, scholarly literature, institutional reports and documented case studies of AI-assisted learning platforms in universities were utilized to elicit data. The findings highlight that AI improves learning by providing content dynamically, providing feedback in real-time, and also tracking learners. But it also points to the dangers of over-trusting automated instructions, reducing learner agency, and focusing on quantifiable performance outcomes. The discussion indicates the necessity of pedagogically based integration of AI that facilitates inquiry, collaboration, and positive struggle and overcomes ethical issues of data privacy, algorithmic bias, and digital inequality. The proposed research paper will help to place AI as facilitative and not as a substitute to human instruction and provide some guidelines on how future research efforts should be performed in an inclusive, explainable, and ethically regulated AI-based learning system. Artificial intelligence Personalised education Critical thinking Introduction Artificial intelligence (AI) may be described as the development of computer programs that can perform the tasks that may be typically performed by human intelligence, which includes reasoning, learning, problem solving, and decision-making. In the past years, AI has penetrated the educational sector and has transformed the existing paradigm of teaching and learning with the emergence of intelligent tutoring systems, adaptive platforms, and data-based learning analytics. The technologies enable the learning environments to be more flexible and responsive and make a transition to the direction of standardized learning and personalized learning experiences. Personalized learning is emphasized by the opportunities to vary the educational materials, pace, and patterns of delivery in line with the needs, capabilities, and interests of the individual learners. Since it has been established that the students differ in terms of their learning styles, previous knowledge and their motivation levels, the individual approach to the matter should assist in boosting the engagement, enhancing the academic performance, and achieving the learner autonomy. The key of this process is the digital space based on AI since it is capable of continuously processing the data of learners and recommending them personal paths of learning and content. Critical thinking as an imperative result of education presupposes the ability to process information and analyze it, evaluate evidence and assumptions and make reasonable decisions. At the time of information overload and fast technological advancement, it is vital to develop a critical attitude to prepare students with both processing even difficult problems and contributing to academic, professional, and societal life. The potential but understudied area of education is AI, personalized learning, and critical thinking. Even though the concept of AI-based personalization might be used to make the learning process more efficient, the competence of AI to assist in the attainment of higher-order thinking skills is a debatable issue that is yet to find the ultimate solution. The research is significant because it does not simply examine the ways the AI can be applied to the personalization of the instruction, but also how the technologies could be modeled and implemented to specifically encourage the critical thinking. Ismaili ( 2024 ) dwells upon the methods of successful artificial intelligence (AI) integration in education. The study is qualitative in type. It will include student, faculty interviews of a Moroccan University of Interdisciplinary Studies and review of the literature on the subject. The findings show that AI possesses colossal prospects to trigger the process of learning and critical thinking. So saying, though, there is indubitably a need in well-organized training and strong institutional support to develop AI literacy. The paper suggests that schools should employ an interdisciplinary approach to the implementation of AI. This includes training programs development, the introduction of AI into the curriculum, and encouragement of partnerships in projects that entail AI. Such measures can be applied to place the students in such a position such that AI will play a crucial role in their working life. The article by Correia et al. (2024) is about the concept of artificial intelligence (AI) implementation in enhancing critical thinking through personalized learning under the contemporary learning environments. The traditional teaching frameworks that rely on the one-size-fits-all principles often cannot possibly be employed to meet the needs of individual learners and limit the development of the higher-order cognitive skills. In reaction, AI-based solutions offer adaptive learning conditions that dynamically respond to individual profile of the students based on their individual students. The question of interest of the study is the following: How can AI be employed in designing critical thinking exercises to individual learning style? The paper is organized around the literature review of 19 peer-reviewed articles identified by Consensus AI academic search engine. The studies were all compared regarding the significance parameters, including relevance, impact, scalability, ethical issues, and student engagement. The findings indicate the ability of AI-driven personalization to improve interaction, induce real-time feedback, and form the critical thinking. The future adoption as well as research is informed by the proposed study which proposes an AI-based model that would integrate individualized paths, problems-centered learning, ethical protection and the teacher-centered approach. Qureshi et al. (2024) article provides the revolutionary role of the Artificial Intelligence (AI) in personalized learning since it can alter the educational process and improve the results of learning. The given paper will provide a glimpse of how AI technologies may be used to tailor the instruction, as well as identify the learning requirements and provide specific strategies that will accommodate the psychological, cognitive, and motivational interests of different students. The article presents the analysis of case studies and examples to highlight different uses of AI to customized learning settings including autonomous tutoring systems, personalized channels of learning, and data-driven analysis. However, AI application in learning is connected to ethical, technological, policy concerns, which require investigative effort and proactive actions to ensure equal access, keep learners’ privacy intact, and eliminate algorithms-based discrimination. The last thing is the conclusion of the article regarding how AI can become transformative in personalized learning and the necessity to cooperate with educators, academics, and policymakers to use the potential of AI and overcome the corresponding challenges. Obaje ( 2025 ) article is concerned with two-sidedness of the AI influence on the formation of critical minds among tertiary students. The paper will be interrogating the AI-promised opportunities in enhancing critical thinking in the academic field by conducting a desktop review. The opportunities are the potentials of the future of AI to give individuals feedback and enable students to think through complex tasks in a deeper manner, the potentials of AI to establish learning communities in which the various viewpoints can be deliberated and examined. The greatest concern is the threat of over-reliance on AI, which can suppress the students regarding their critical and autonomous thinking ability. This article comes out as a middle ground on AI as a tool, which may be utilized to enhance critical thinking, and minimize risks associated with the usage of AI in higher education providing an involved review of available literature and an overview of the current AI application to the sphere of higher education. The paper concludes by giving the suggestions that ought to be embraced by teachers and other interested parties on how they can effectively introduce AI in our curriculum without interfering with the fundamentals of learning. In a book chapter, Asrifan et al. ( 2025 ) explain that Artificial Intelligence (AI) has a transformational effect in learning institutions to help them develop the ability to think critically. It reveals that Knewton and Carnegie Learning which are AI-driven learning applications give personalized interactive learning platforms for increasing student interaction and their query. On these platforms, the ability to analyze, assess, and synthesize the content on a competent level is also encouraged due to the ability to support the existence of various learning styles and provide students with particular feedback. The threats of over-reliance on AI are also addressed in the chapter and the need to have a less vigorous manner of doing the same but encouraging people to find their own solutions is also indicated. The use of AI in education can improve the academic success of students and equip them with essential competencies of making informed choices and solving problems in an intricate modern environment. However, the present study examines how AI may be used in the context of individualised education and creating critical thinking by implementing a SWOT (Strengths, Weaknesses, Opportunities and Threats) analytical model to frame the research in a systematic manner when studying the impact of AI on education. Addressing this role, the study can contribute to a more adequate understanding of how intelligent systems can be used in improving efficiency and intellectual growth of the present education systems. Statement of the Problem Finding a middle ground between the personalization of the Algorithms and the Development of the Critical Thinking in the Artificial Intelligence-based Learning Research Objectives To propose a strategic framework of the effective educational implementation of AI based on the outcomes of SWOT, the following objectives should be achieved. To identify the possibilities of AI in enhancing an individual learning process and critical thinking To investigate the drawbacks that are associated with the use of AI in encouraging higher-order thinking abilities To examine the opportunities of integrating AI with the pedagogical practices that will facilitate the analytical and reflective learning. To investigate potential threats and ethical concerns related to the application of AI to the educational sector Research Methodology (SWOT Analysis Approach) The SWOT analysis framework will be used in this paper as a qualitative and analytic research study to evaluate the effects of AI in customized studying and how it influences critical thinking. The SWOT analysis will make it possible to perform a systematic investigation of the internal and external factors that affect the effectiveness of AI-based educational technologies. The data of analysis was collected in the academic literature, institutional reports, student answers and attitude of the educators. The case studies associated with the AI-assisted educational system in higher education were reviewed to identify the application and outcomes in a real-life setting. The analysis was summarized into four dimensions: Strengths and the merits or advantages of AI to catalyse personalised learning, Weaknesses, which are viewed in the internal limitations of the pedagogical design, reliance of the learners and limitation of the system, Opportunities, the acceptance of external conditions, which make innovation easier, policy building and curriculum integration and Threats, the assessment of the external threats such as ethical problems, data privacy, and digital inequality These findings were provided with the thematic groupings on each of the SWOT dimensions to generate a strategic picture of the significance of AI in facilitating critical thinking within individualized learning environments. Results The analysis provided in the form of SWOT revealed that the application of the artificial intelligence (AI) has much to contribute to the improvement of the personalized learning experience as the platform has the capacity to introduce and provide adaptive teaching material, provide real-time feedback continuously, and detail and learner-specific progress tracking systems. The specified strengths will ensure that the AI-driven platforms will dynamically adjust the scope of the challenges (Dutta et al., 2024), the learning pace, and the type of support in the learning process depending on the performance of the individual students (Strielkowski et al., 2025 ) and their learning preferences. As a result, the conceptual misconceptions can be more likely to be identified, the background information can be reviewed, and academic content will be worked with in the appropriate cognitive level, which can be one of the motivating factors (Scotkovsky, 2025 ) and promote the long-term engagement. Furthermore, the availability of information-based insights allows teachers to get a more comprehensive view of student learning patterns, hence providing more guided pedagogical reactions and academic assistance. On the one hand, these positive aspects are present; however, the evaluation has also found that there are several gaps that could render the application of AI less efficient in terms of cultivating a new level of skills related to critical thinking. The second problem is that certain AI systems are prone to be interested in such performance indicators as accuracy rates, completion time, and automated scoring values (Mortaji and Sadeghi, 2024 ) at the expense of qualitative aspects of learning, reflective thinking, and innovative problem solving. This can create an unintentional emphasis on the quantifiable outcomes, and such methods that encourage students to seek to find the correct answer, rather than to familiarize themselves with the conceptual base. It can, furthermore, be created with fewer choices to solve complicated problems in a constructive way due to the application of algorithm-generated hints and step-by-step guidelines, which although beneficial to scaffolding, can restrict the aim of a problem (Yu, 2020 ). In some cases, students have become more addicted to the assistance being automated and this is restricting the development of analytical and independent inquiry (Karamuk, 2025 ). As well, it was observed that AI holds immense possibilities of enhancing its operations through the integration of it with inquiry and collaborative learning models. The technology can assist the educators in stimulating discussion, argumentation, and project-based learning using the AI tools as parts of the online platform, which also promotes dialogue, collaborative effort, and addressing real-world issues (Oprea et al., 2020 ). The incorporation of AI systems into the curriculum model that places high-order cognitive processes to even a greater extent offers further possibilities to change the concept of AI application to the realm of the instructional support to the more facilitative mode of knowledge construction. Moreover, new national and institution policies are involved, which are concerned with digital education, teacher professional development which further add to the context. Educational technologies can also be ethically applied to define the facilitative environment of responsible and efficient application of AI in classrooms (Chan et al., 2025). In the meantime, the analysis has shown that there are numerous critical threats that could withdraw the equitable and sustainable use of AI in the individualized education. The revelation of data privacy and security are also still eminent with respect to recording, storage and utilization of sensitive student data. Another risk is related to algorithmic bias (Bose, 2025 ), when the biased training content or design choices may give different groups of students differing learning recommendations or assessment scores. Besides, lack of even access to stable digital infrastructure, devices and internet connectivity can increase the existing educational disparities, limiting the benefits of AI-based individualisation to the more privileged. The transparency, accountability, and informed consent are also frequent challenges (Kamila and Jasrotia, 2025 ), and the effective regulatory mechanisms and regular monitoring of AI use in the educational process should also be implemented successfully. Table 1 SWOT Matrix: Artificial Intelligence Strategic Analysis of Personalized Education and Development of Critical Thinking Strengths (S) Weaknesses (W) Encourages adaptive instructions according to the level of cognition and learner readiness. More motivation should be given through feedback and interactive content. Provides information analytics in order to make wise curriculum-related changes. Accesses a broader association of different and distanced learners. Possible transparent learning analytics. Enables evaluation in the form of formative and continual. By applying guided and prewritten instructions, learning autonomy could be reduced. Favours chain ganging on AI-generated proposals. Poor adaptability of artificial intelligence based content models. Highly costly training and infrastructure. Black box decision making procedures. Poor concentration on quantifiable performance indicators. Opportunities (O) Threats (T) Permits inquiry-based, problem-based and reflective paradigms of learning. Promotes the usage of digital communities of learning. Bias AI co-design and instructor-based learning solutions. The policy-based investment of digital inclusion. Rationality and explainability of AI models. Artificial intelligence performance-based evaluation and genuine assessment. Algorithmic biasness leading to obstacle of learning Online exhaustion and reduced bodily communication. Inequality or insufficiency between artificial intelligence and institutional curricula. Digital disparity along socio- Economic lines. Threat to information security and non-observation of regulations. Ethical issues i.e. abuse of AI-generated material. Table 2 Strategic Interpretation Strategy Type Applications in AI-Based Education SO Strategies (Maximize opportunities with strengths) WO Strategies (Utilize the chances to address the shortcomings) ST Strategies (Threats would be mitigated with the help of strengths) WT Strategies (Minimize the threats and weaknesses) Learn with the assistance of adaptive systems which can be applied to implement inquiry-based and collaborative models of learning. The training steps should be implemented to the teachers in order to reduce overreliance on computer-created instructions. Use visible analytics to avoid the risks of ethical and privacy. Proper designing of institutional AI policies and also the formulation of equal access. Discussion This research project demonstrates the bi-dimensional model of artificial intelligence (AI) in personalized education as the source of adaptive learning and as a potential inhibitor to the process of the formation of critical thinking. The main strengths that can be identified within the SWOT-based analysis include the fact that AI-driven systems can guarantee the significant change in the manner of how learners peruse the content, especially, by offering the relevant content, pacing, and feedback. This correlates with the theories of constructivist and learner-centered pedagogy theories which emphasize the necessity to contextualize the learning in the set of knowledge of the learner. The AI technologies can facilitate the more effective and reactive teaching, as one will be able to recognize the conceptual gaps on time and provide the particular instructional assistance that can facilitate the student body and the educators in their work and meeting of the academic objectives. However, the disadvantages mentioned in the overvaluation of the performance measures and the computer-generated instructions raise the serious pedagogical concerns. Even though quantitative data such as accuracy rates and completion rates provide persons with a good guide to track the progress, the statistics cannot provide one with an opportunity to comprehend more than a superficial process such as reasoning, synthesis and evaluation. The reason why the learners tend to turn to the hints given by the algorithms is that it means that there is the risk of diminishing productive struggle that is usually viewed as one of the major factors of developing higher-order thinking. This finding confirms the ongoing debate about the educational technology research on balancing between the scaffolds in the instructions and the autonomy of the learners, highlighting the need of AI systems that will be able to stimulate the reflection of the learners rather than focus on optimizing the task performance. The specified opportunities imply that AI will be less individualized in content delivery and will be more facilitative in the collaborative learning process and an inquiry-driven process. By incorporating AI into the digital platforms, which enable peer-to-peer interaction and debate and problem-solving, the divide between personalization and social learning can be bridged. Such a practice assists in development of critical thinking in that the learning communities are oriented in communities of practice where ideas are exchanged, criticized and perfected. Additionally, one can ensure that the personalization plans are underpinned by pedagogical underpinnings rather than being technologically driven alone once the AI tools have been adjusted to the curricula models that promote the significance of analytical and evaluating skills. At the same time, the threats of information confidentiality, bias algorithms, unequal access reveal the socio-ethical nature of the use of AI in education, on the whole. These issues draw attention to the fact that the effectiveness of AI-based personalization is not the question of technical feasibility but also of control, policies, and institutional readiness. This transparency/accountability gap brings up the question of educators, policymakers and learners’ involvement into the method of building and running AI systems. Without these protections, it is real that AI can indeed be a continuation of existing disparities rather than being among the inclusive and fair procedures in education. Overall, the discussion suggests that a successful implementation of AI in personalised learning should be based on a moderate approach that would utilise the potential of the technology, yet would take the initiative to address pedagogical, ethical and contextual limitations. Therefore, when the AI is introduced in the role of an assistant, rather than a pedagogical force, educators can harness its potential to address the demands of both differentiated instruction and that of critical thinking that learners must exercise with a difficult academic and real-world challenges. Contribution to Knowledge The study will contribute the strategic level of SWOT-based method to the work of AI involvement into the process of personal education and development of critical thinking. It contributes to the existing literature since it performs a systematic mapping of internal and external circumstances that influence the pedagogical effectiveness of AI-based learning environments. The research offers the theoretical backdrop within which AI is introduced as the facilitative mechanism, rather than the process, which will displace the human instruction, and the intermediate role of teachers in the technology implementation. It also provides policy and curriculum developers with a good experience to strike a balance between AI adoption and cognitive, ethical and equity-based education goals. Future Prospects of Research Empirical research can be used in future research to validate the SWOT findings using large sample surveys, experimental research and longitudinal research. There is also the opportunity to further understand diverse impacts of AI based on the comparative research on different levels of education, disciplines, and socio-economic statuses.The chance to create clear and interpretable AI models that could help learners to understand and make judgments about recommendations provided by algorithms can also be utilized. Other potential areas of research are inclusive AI policies which would contribute to finding a solution to the issue of digital divides and aid access to more individualized learning technologies. Conclusion The SWOT analysis demonstrates that the Artificial Intelligence can have tremendous opportunities to introduce the individual approach to learning and assist learners in developing the skill of critical thinking. It possesses powerful elements of flexibility, feedback, and learner engagement that may lead to the deeper thinking when combined with the productive pedagogical strategies. However, excessive reliance on automation and threats, such as threats to ethics and digital inequality, are considered the areas of weakness and demonstrate that it has to be performed with care and plans. To ensure that personalization does not compromise the critical thinking process, the moderate approach should be used: it implies the incorporation of AI with inquiry-based learning, open systems, and comprehensive policies. Lastly, the future of AI in education should be anchored on collaboration between educators, developers, and policymakers in a bid to create learning environments that are founded not only on the newest technologies but also on the intellectual and moral assumptions. Declarations Funding The authors received no financial support for the research, authorship, or publication of this article. Corresponding authors: Dr. Nandini Banerjee and Dr. Madan Singh Deupa Ethics, Consent to Participate, and Consent to Publish declarations : Ethics Statement This experiment was not carried out by experimenting directly with human subjects or live vertebrates. This study is purely theoretical and conceptual in nature and is based on a qualitative SWOT analysis of previously published academic literature, institutional reports, and case studies related to AI-assisted learning platforms. The research relies exclusively on publicly available secondary data with no personal identification of individuals. As no direct interaction with human participants was involved and no primary data were collected, formal approval from an Institutional Review Board (IRB) or ethics committee was not required for this study. So, “ Ethics Statement ” is not applicable. Consent to Participate The study did not require the direct collection of primary data using human participants; therefore, informed consent to take part was not possible. There was no human subject and primary data collection in this study. So, “ Consent to Participate ” is not applicable . Consent to Publish No personal information, details, or pictures of individual participants can be identified in the manuscript. So, “Consent to Publish” is not applicable . Data Availability Statement. The study is founded on the interpretation of available academic literature and case studies that are described in the academic databases and mentioned in the sources of references list that are publically accessible. So, “ Data Availability Statement ” is not applicable . References Asrifan A, Khristianto K, Budiman A, Astuti PI, Rossydi A. (2025). Enhancing Critical Thinking: The Role of AI in Modern Education. In Enhancing Classroom Instruction and Student Skills with AI. IGI Global Sci Publishing, 413–48. https://www.igi-global.com/chapter/enhancing-critical-thinking/381083 Bose M. (2025). Bias in AI: A societal threat: A look beyond the tech. In Open AI and Computational Intelligence for Society 5.0 (pp. 197–224). IGI Global Scientific Publishing. ttps://doi.org/10.4018/979-8-3693-4326-5.ch009 Chan MM, Rosales M, Hernandez-Rizzardini R, Amado-Salvatierra HR. (2025, April). Ethical AI in Education: A Proposed Model for Responsible Integration. In 2025 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–7). 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor invited by journal 19 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8969990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618813694,"identity":"46e862f9-38b6-41cf-9c5c-f2b6e7fdfa08","order_by":0,"name":"Dr. Nandini Banerjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYJACiQQGCQYDMJOHgYGfByrMRrQWyR5itIAIAxjP4AwBR5mznz144+EOCzDjcYWMjZzxmbPHJBhq7Bj4pBuwarHsyUu2SDwjAWYYnuFJMzY725cmwXAsmYFN5gBWLQYHcswkEtskwAzJBp7DidvO85hJMLAdYGAD+hGrlvNvoFrOvzH/2cDzv35zP0jLPzxabsBsATIYG3gOJBjw9phJMLbh0/LG2AKohcdyxhtjoMOSDWecOQMU6Uvmwe2wHMObP9vq5Mz5cww/NvbYyfP35Bje+PDNTk5+BnYtMACJckZYNCbARAiDH0SqGwWjYBSMghEFAPiwUrsbyYNWAAAAAElFTkSuQmCC","orcid":"","institution":"Kazi Nazrul University","correspondingAuthor":true,"prefix":"Dr.","firstName":"Nandini","middleName":"","lastName":"Banerjee","suffix":""},{"id":618813695,"identity":"33121ec3-5aae-4fab-9201-68911f5aab7c","order_by":1,"name":"Susmita Rakshit","email":"","orcid":"","institution":"Kazi Nazrul University","correspondingAuthor":false,"prefix":"","firstName":"Susmita","middleName":"","lastName":"Rakshit","suffix":""},{"id":618813696,"identity":"9ce34a5b-07ce-4ec6-bbc8-999c2bf9ced1","order_by":2,"name":"Dr. Madan Singh Deupa","email":"","orcid":"","institution":"Far-western University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Madan","middleName":"Singh","lastName":"Deupa","suffix":""}],"badges":[],"createdAt":"2026-02-25 16:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8969990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8969990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106642530,"identity":"4e71f618-28e9-40de-a814-0dacdb32a4bf","added_by":"auto","created_at":"2026-04-10 18:41:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":552357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8969990/v1/15d4bca0-0347-4183-ae48-56dc40030978.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence in Personalised Education with the Development of Critical Thinking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) may be described as the development of computer programs that can perform the tasks that may be typically performed by human intelligence, which includes reasoning, learning, problem solving, and decision-making. In the past years, AI has penetrated the educational sector and has transformed the existing paradigm of teaching and learning with the emergence of intelligent tutoring systems, adaptive platforms, and data-based learning analytics. The technologies enable the learning environments to be more flexible and responsive and make a transition to the direction of standardized learning and personalized learning experiences. Personalized learning is emphasized by the opportunities to vary the educational materials, pace, and patterns of delivery in line with the needs, capabilities, and interests of the individual learners. Since it has been established that the students differ in terms of their learning styles, previous knowledge and their motivation levels, the individual approach to the matter should assist in boosting the engagement, enhancing the academic performance, and achieving the learner autonomy. The key of this process is the digital space based on AI since it is capable of continuously processing the data of learners and recommending them personal paths of learning and content. Critical thinking as an imperative result of education presupposes the ability to process information and analyze it, evaluate evidence and assumptions and make reasonable decisions. At the time of information overload and fast technological advancement, it is vital to develop a critical attitude to prepare students with both processing even difficult problems and contributing to academic, professional, and societal life. The potential but understudied area of education is AI, personalized learning, and critical thinking. Even though the concept of AI-based personalization might be used to make the learning process more efficient, the competence of AI to assist in the attainment of higher-order thinking skills is a debatable issue that is yet to find the ultimate solution. The research is significant because it does not simply examine the ways the AI can be applied to the personalization of the instruction, but also how the technologies could be modeled and implemented to specifically encourage the critical thinking.\u003c/p\u003e \u003cp\u003eIsmaili (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) dwells upon the methods of successful artificial intelligence (AI) integration in education. The study is qualitative in type. It will include student, faculty interviews of a Moroccan University of Interdisciplinary Studies and review of the literature on the subject. The findings show that AI possesses colossal prospects to trigger the process of learning and critical thinking. So saying, though, there is indubitably a need in well-organized training and strong institutional support to develop AI literacy. The paper suggests that schools should employ an interdisciplinary approach to the implementation of AI. This includes training programs development, the introduction of AI into the curriculum, and encouragement of partnerships in projects that entail AI. Such measures can be applied to place the students in such a position such that AI will play a crucial role in their working life. The article by Correia et al. (2024) is about the concept of artificial intelligence (AI) implementation in enhancing critical thinking through personalized learning under the contemporary learning environments. The traditional teaching frameworks that rely on the one-size-fits-all principles often cannot possibly be employed to meet the needs of individual learners and limit the development of the higher-order cognitive skills. In reaction, AI-based solutions offer adaptive learning conditions that dynamically respond to individual profile of the students based on their individual students. The question of interest of the study is the following: How can AI be employed in designing critical thinking exercises to individual learning style? The paper is organized around the literature review of 19 peer-reviewed articles identified by Consensus AI academic search engine. The studies were all compared regarding the significance parameters, including relevance, impact, scalability, ethical issues, and student engagement. The findings indicate the ability of AI-driven personalization to improve interaction, induce real-time feedback, and form the critical thinking. The future adoption as well as research is informed by the proposed study which proposes an AI-based model that would integrate individualized paths, problems-centered learning, ethical protection and the teacher-centered approach. Qureshi et al. (2024) article provides the revolutionary role of the Artificial Intelligence (AI) in personalized learning since it can alter the educational process and improve the results of learning. The given paper will provide a glimpse of how AI technologies may be used to tailor the instruction, as well as identify the learning requirements and provide specific strategies that will accommodate the psychological, cognitive, and motivational interests of different students. The article presents the analysis of case studies and examples to highlight different uses of AI to customized learning settings including autonomous tutoring systems, personalized channels of learning, and data-driven analysis. However, AI application in learning is connected to ethical, technological, policy concerns, which require investigative effort and proactive actions to ensure equal access, keep learners’ privacy intact, and eliminate algorithms-based discrimination. The last thing is the conclusion of the article regarding how AI can become transformative in personalized learning and the necessity to cooperate with educators, academics, and policymakers to use the potential of AI and overcome the corresponding challenges. Obaje (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) article is concerned with two-sidedness of the AI influence on the formation of critical minds among tertiary students. The paper will be interrogating the AI-promised opportunities in enhancing critical thinking in the academic field by conducting a desktop review. The opportunities are the potentials of the future of AI to give individuals feedback and enable students to think through complex tasks in a deeper manner, the potentials of AI to establish learning communities in which the various viewpoints can be deliberated and examined. The greatest concern is the threat of over-reliance on AI, which can suppress the students regarding their critical and autonomous thinking ability. This article comes out as a middle ground on AI as a tool, which may be utilized to enhance critical thinking, and minimize risks associated with the usage of AI in higher education providing an involved review of available literature and an overview of the current AI application to the sphere of higher education. The paper concludes by giving the suggestions that ought to be embraced by teachers and other interested parties on how they can effectively introduce AI in our curriculum without interfering with the fundamentals of learning. In a book chapter, Asrifan et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) explain that Artificial Intelligence (AI) has a transformational effect in learning institutions to help them develop the ability to think critically. It reveals that Knewton and Carnegie Learning which are AI-driven learning applications give personalized interactive learning platforms for increasing student interaction and their query. On these platforms, the ability to analyze, assess, and synthesize the content on a competent level is also encouraged due to the ability to support the existence of various learning styles and provide students with particular feedback. The threats of over-reliance on AI are also addressed in the chapter and the need to have a less vigorous manner of doing the same but encouraging people to find their own solutions is also indicated. The use of AI in education can improve the academic success of students and equip them with essential competencies of making informed choices and solving problems in an intricate modern environment.\u003c/p\u003e \u003cp\u003eHowever, the present study examines how AI may be used in the context of individualised education and creating critical thinking by implementing a SWOT (Strengths, Weaknesses, Opportunities and Threats) analytical model to frame the research in a systematic manner when studying the impact of AI on education. Addressing this role, the study can contribute to a more adequate understanding of how intelligent systems can be used in improving efficiency and intellectual growth of the present education systems.\u003c/p\u003e\n\u003ch3\u003eStatement of the Problem\u003c/h3\u003e\n\u003cp\u003eFinding a middle ground between the personalization of the Algorithms and the Development of the Critical Thinking in the Artificial Intelligence-based Learning\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Objectives\u003c/h2\u003e \u003cp\u003eTo propose a strategic framework of the effective educational implementation of AI based on the outcomes of SWOT, the following objectives should be achieved.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the possibilities of AI in enhancing an individual learning process and critical thinking\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo investigate the drawbacks that are associated with the use of AI in encouraging higher-order thinking abilities\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine the opportunities of integrating AI with the pedagogical practices that will facilitate the analytical and reflective learning.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo investigate potential threats and ethical concerns related to the application of AI to the educational sector\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/div\u003e\n\n "},{"header":"Research Methodology (SWOT Analysis Approach)","content":"\u003cp\u003eThe SWOT analysis framework will be used in this paper as a qualitative and analytic research study to evaluate the effects of AI in customized studying and how it influences critical thinking. The SWOT analysis will make it possible to perform a systematic investigation of the internal and external factors that affect the effectiveness of AI-based educational technologies. The data of analysis was collected in the academic literature, institutional reports, student answers and attitude of the educators. The case studies associated with the AI-assisted educational system in higher education were reviewed to identify the application and outcomes in a real-life setting.\u003c/p\u003e\u003cp\u003eThe analysis was summarized into four dimensions: Strengths and the merits or advantages of AI to catalyse personalised learning, Weaknesses, which are viewed in the internal limitations of the pedagogical design, reliance of the learners and limitation of the system, Opportunities, the acceptance of external conditions, which make innovation easier, policy building and curriculum integration and Threats, the assessment of the external threats such as ethical problems, data privacy, and digital inequality\u003c/p\u003e\u003cp\u003eThese findings were provided with the thematic groupings on each of the SWOT dimensions to generate a strategic picture of the significance of AI in facilitating critical thinking within individualized learning environments.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis provided in the form of SWOT revealed that the application of the artificial intelligence (AI) has much to contribute to the improvement of the personalized learning experience as the platform has the capacity to introduce and provide adaptive teaching material, provide real-time feedback continuously, and detail and learner-specific progress tracking systems. The specified strengths will ensure that the AI-driven platforms will dynamically adjust the scope of the challenges (Dutta et al., 2024), the learning pace, and the type of support in the learning process depending on the performance of the individual students (Strielkowski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and their learning preferences. As a result, the conceptual misconceptions can be more likely to be identified, the background information can be reviewed, and academic content will be worked with in the appropriate cognitive level, which can be one of the motivating factors (Scotkovsky, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and promote the long-term engagement. Furthermore, the availability of information-based insights allows teachers to get a more comprehensive view of student learning patterns, hence providing more guided pedagogical reactions and academic assistance.\u003c/p\u003e \u003cp\u003eOn the one hand, these positive aspects are present; however, the evaluation has also found that there are several gaps that could render the application of AI less efficient in terms of cultivating a new level of skills related to critical thinking. The second problem is that certain AI systems are prone to be interested in such performance indicators as accuracy rates, completion time, and automated scoring values (Mortaji and Sadeghi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) at the expense of qualitative aspects of learning, reflective thinking, and innovative problem solving. This can create an unintentional emphasis on the quantifiable outcomes, and such methods that encourage students to seek to find the correct answer, rather than to familiarize themselves with the conceptual base. It can, furthermore, be created with fewer choices to solve complicated problems in a constructive way due to the application of algorithm-generated hints and step-by-step guidelines, which although beneficial to scaffolding, can restrict the aim of a problem (Yu, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In some cases, students have become more addicted to the assistance being automated and this is restricting the development of analytical and independent inquiry (Karamuk, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs well, it was observed that AI holds immense possibilities of enhancing its operations through the integration of it with inquiry and collaborative learning models. The technology can assist the educators in stimulating discussion, argumentation, and project-based learning using the AI tools as parts of the online platform, which also promotes dialogue, collaborative effort, and addressing real-world issues (Oprea et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The incorporation of AI systems into the curriculum model that places high-order cognitive processes to even a greater extent offers further possibilities to change the concept of AI application to the realm of the instructional support to the more facilitative mode of knowledge construction. Moreover, new national and institution policies are involved, which are concerned with digital education, teacher professional development which further add to the context. Educational technologies can also be ethically applied to define the facilitative environment of responsible and efficient application of AI in classrooms (Chan et al., 2025).\u003c/p\u003e \u003cp\u003eIn the meantime, the analysis has shown that there are numerous critical threats that could withdraw the equitable and sustainable use of AI in the individualized education. The revelation of data privacy and security are also still eminent with respect to recording, storage and utilization of sensitive student data. Another risk is related to algorithmic bias (Bose, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), when the biased training content or design choices may give different groups of students differing learning recommendations or assessment scores. Besides, lack of even access to stable digital infrastructure, devices and internet connectivity can increase the existing educational disparities, limiting the benefits of AI-based individualisation to the more privileged. The transparency, accountability, and informed consent are also frequent challenges (Kamila and Jasrotia, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and the effective regulatory mechanisms and regular monitoring of AI use in the educational process should also be implemented successfully.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSWOT Matrix: Artificial Intelligence Strategic Analysis of Personalized Education and Development of Critical Thinking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrengths (S)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeaknesses (W)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncourages adaptive instructions according to the level of cognition and learner readiness.\u003c/p\u003e \u003cp\u003eMore motivation should be given through feedback and interactive content.\u003c/p\u003e \u003cp\u003eProvides information analytics in order to make wise curriculum-related changes.\u003c/p\u003e \u003cp\u003eAccesses a broader association of different and distanced learners.\u003c/p\u003e \u003cp\u003ePossible transparent learning analytics.\u003c/p\u003e \u003cp\u003eEnables evaluation in the form of formative and continual.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBy applying guided and prewritten instructions, learning autonomy could be reduced.\u003c/p\u003e \u003cp\u003eFavours chain ganging on AI-generated proposals.\u003c/p\u003e \u003cp\u003ePoor adaptability of artificial intelligence based content models.\u003c/p\u003e \u003cp\u003eHighly costly training and infrastructure.\u003c/p\u003e \u003cp\u003eBlack box decision making procedures.\u003c/p\u003e \u003cp\u003ePoor concentration on quantifiable performance indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpportunities (O)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eThreats (T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePermits inquiry-based, problem-based and reflective paradigms of learning.\u003c/p\u003e \u003cp\u003ePromotes the usage of digital communities of learning.\u003c/p\u003e \u003cp\u003eBias AI co-design and instructor-based learning solutions.\u003c/p\u003e \u003cp\u003eThe policy-based investment of digital inclusion.\u003c/p\u003e \u003cp\u003eRationality and explainability of AI models.\u003c/p\u003e \u003cp\u003eArtificial intelligence performance-based evaluation and genuine assessment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgorithmic biasness leading to obstacle of learning\u003c/p\u003e \u003cp\u003eOnline exhaustion and reduced bodily communication.\u003c/p\u003e \u003cp\u003eInequality or insufficiency between artificial intelligence and institutional curricula.\u003c/p\u003e \u003cp\u003eDigital disparity along socio- Economic lines.\u003c/p\u003e \u003cp\u003eThreat to information security and non-observation of regulations.\u003c/p\u003e \u003cp\u003eEthical issues i.e. abuse of AI-generated material.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStrategic Interpretation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrategy Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplications in AI-Based Education\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO Strategies\u003c/p\u003e \u003cp\u003e(Maximize opportunities with strengths)\u003c/p\u003e \u003cp\u003eWO Strategies\u003c/p\u003e \u003cp\u003e(Utilize the chances to address the shortcomings)\u003c/p\u003e \u003cp\u003eST Strategies\u003c/p\u003e \u003cp\u003e(Threats would be mitigated with the help of strengths)\u003c/p\u003e \u003cp\u003eWT Strategies\u003c/p\u003e \u003cp\u003e(Minimize the threats and weaknesses)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearn with the assistance of adaptive systems which can be applied to implement inquiry-based and collaborative models of learning.\u003c/p\u003e \u003cp\u003eThe training steps should be implemented to the teachers in order to reduce overreliance on computer-created instructions.\u003c/p\u003e \u003cp\u003eUse visible analytics to avoid the risks of ethical and privacy.\u003c/p\u003e \u003cp\u003eProper designing of institutional AI policies and also the formulation of equal access.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research project demonstrates the bi-dimensional model of artificial intelligence (AI) in personalized education as the source of adaptive learning and as a potential inhibitor to the process of the formation of critical thinking. The main strengths that can be identified within the SWOT-based analysis include the fact that AI-driven systems can guarantee the significant change in the manner of how learners peruse the content, especially, by offering the relevant content, pacing, and feedback. This correlates with the theories of constructivist and learner-centered pedagogy theories which emphasize the necessity to contextualize the learning in the set of knowledge of the learner. The AI technologies can facilitate the more effective and reactive teaching, as one will be able to recognize the conceptual gaps on time and provide the particular instructional assistance that can facilitate the student body and the educators in their work and meeting of the academic objectives.\u003c/p\u003e \u003cp\u003eHowever, the disadvantages mentioned in the overvaluation of the performance measures and the computer-generated instructions raise the serious pedagogical concerns. Even though quantitative data such as accuracy rates and completion rates provide persons with a good guide to track the progress, the statistics cannot provide one with an opportunity to comprehend more than a superficial process such as reasoning, synthesis and evaluation. The reason why the learners tend to turn to the hints given by the algorithms is that it means that there is the risk of diminishing productive struggle that is usually viewed as one of the major factors of developing higher-order thinking. This finding confirms the ongoing debate about the educational technology research on balancing between the scaffolds in the instructions and the autonomy of the learners, highlighting the need of AI systems that will be able to stimulate the reflection of the learners rather than focus on optimizing the task performance.\u003c/p\u003e \u003cp\u003eThe specified opportunities imply that AI will be less individualized in content delivery and will be more facilitative in the collaborative learning process and an inquiry-driven process. By incorporating AI into the digital platforms, which enable peer-to-peer interaction and debate and problem-solving, the divide between personalization and social learning can be bridged. Such a practice assists in development of critical thinking in that the learning communities are oriented in communities of practice where ideas are exchanged, criticized and perfected. Additionally, one can ensure that the personalization plans are underpinned by pedagogical underpinnings rather than being technologically driven alone once the AI tools have been adjusted to the curricula models that promote the significance of analytical and evaluating skills.\u003c/p\u003e \u003cp\u003eAt the same time, the threats of information confidentiality, bias algorithms, unequal access reveal the socio-ethical nature of the use of AI in education, on the whole. These issues draw attention to the fact that the effectiveness of AI-based personalization is not the question of technical feasibility but also of control, policies, and institutional readiness. This transparency/accountability gap brings up the question of educators, policymakers and learners\u0026rsquo; involvement into the method of building and running AI systems. Without these protections, it is real that AI can indeed be a continuation of existing disparities rather than being among the inclusive and fair procedures in education.\u003c/p\u003e \u003cp\u003eOverall, the discussion suggests that a successful implementation of AI in personalised learning should be based on a moderate approach that would utilise the potential of the technology, yet would take the initiative to address pedagogical, ethical and contextual limitations. Therefore, when the AI is introduced in the role of an assistant, rather than a pedagogical force, educators can harness its potential to address the demands of both differentiated instruction and that of critical thinking that learners must exercise with a difficult academic and real-world challenges.\u003c/p\u003e\n\u003ch3\u003eContribution to Knowledge\u003c/h3\u003e\n\u003cp\u003eThe study will contribute the strategic level of SWOT-based method to the work of AI involvement into the process of personal education and development of critical thinking. It contributes to the existing literature since it performs a systematic mapping of internal and external circumstances that influence the pedagogical effectiveness of AI-based learning environments. The research offers the theoretical backdrop within which AI is introduced as the facilitative mechanism, rather than the process, which will displace the human instruction, and the intermediate role of teachers in the technology implementation. It also provides policy and curriculum developers with a good experience to strike a balance between AI adoption and cognitive, ethical and equity-based education goals.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFuture Prospects of Research\u003c/h2\u003e \u003cp\u003eEmpirical research can be used in future research to validate the SWOT findings using large sample surveys, experimental research and longitudinal research. There is also the opportunity to further understand diverse impacts of AI based on the comparative research on different levels of education, disciplines, and socio-economic statuses.The chance to create clear and interpretable AI models that could help learners to understand and make judgments about recommendations provided by algorithms can also be utilized. Other potential areas of research are inclusive AI policies which would contribute to finding a solution to the issue of digital divides and aid access to more individualized learning technologies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe SWOT analysis demonstrates that the Artificial Intelligence can have tremendous opportunities to introduce the individual approach to learning and assist learners in developing the skill of critical thinking. It possesses powerful elements of flexibility, feedback, and learner engagement that may lead to the deeper thinking when combined with the productive pedagogical strategies. However, excessive reliance on automation and threats, such as threats to ethics and digital inequality, are considered the areas of weakness and demonstrate that it has to be performed with care and plans. To ensure that personalization does not compromise the critical thinking process, the moderate approach should be used: it implies the incorporation of AI with inquiry-based learning, open systems, and comprehensive policies. Lastly, the future of AI in education should be anchored on collaboration between educators, developers, and policymakers in a bid to create learning environments that are founded not only on the newest technologies but also on the intellectual and moral assumptions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors received no financial support for the research, authorship, or publication of this article.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCorresponding authors:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003eDr. Nandini Banerjee and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDr.\u0026nbsp;Madan\u0026nbsp;Singh Deupa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experiment was not carried out by experimenting directly with human subjects or live vertebrates. This study is purely theoretical and conceptual in nature and is based on a qualitative SWOT analysis of previously published academic literature, institutional reports, and case studies related to AI-assisted learning platforms. The research relies exclusively on publicly available secondary data with no personal identification of individuals. As no direct interaction with human participants was involved and no primary data were collected, formal approval from an Institutional Review Board (IRB) or ethics committee was not required for this study.\u003c/p\u003e\n\u003cp\u003eSo, \u0026ldquo;\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u0026rdquo;\u003cem\u003e\u0026nbsp;is\u0026nbsp;\u003c/em\u003e\u003cem\u003enot applicable.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not require the direct collection of primary data using human participants; therefore, informed consent to take part was not possible. There was no human subject and primary data collection in this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSo, \u0026ldquo;\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026rdquo; \u003cem\u003eis not applicable\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo personal information, details, or pictures of individual participants can be identified in the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSo, \u0026ldquo;Consent to Publish\u0026rdquo; \u003cem\u003eis not applicable\u003c/em\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study is founded on the interpretation of available academic literature and case studies that are described in the academic databases and mentioned in the sources of references list that are publically accessible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSo, \u0026ldquo;\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u0026rdquo; \u003cem\u003eis not applicable\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsrifan A, Khristianto K, Budiman A, Astuti PI, Rossydi A. 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Fla L Rev. 2020;72:331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scholarship.law.ufl.edu/flr/vol72/iss2/4\u003c/span\u003e\u003cspan address=\"https://scholarship.law.ufl.edu/flr/vol72/iss2/4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, Personalised education, Critical thinking","lastPublishedDoi":"10.21203/rs.3.rs-8969990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8969990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence (AI) has transformed modern-day education by facilitating customized learning experiences, but there are still issues about how it affects the growth of critical thinking skills. This experiment touches upon the issue of the tradeoff between algorithm-based personalization and the development of more advanced cognitive functions of AI-assisted learning. The study sets out to investigate the strengths, weaknesses, opportunities and threats of AI in personalised education, define pedagogical and ethical issues and give strategic suggestions of how AI can be used with reflective and analytical pedagogy. Based on a qualitative SWOT analysis model, scholarly literature, institutional reports and documented case studies of AI-assisted learning platforms in universities were utilized to elicit data. The findings highlight that AI improves learning by providing content dynamically, providing feedback in real-time, and also tracking learners. But it also points to the dangers of over-trusting automated instructions, reducing learner agency, and focusing on quantifiable performance outcomes. The discussion indicates the necessity of pedagogically based integration of AI that facilitates inquiry, collaboration, and positive struggle and overcomes ethical issues of data privacy, algorithmic bias, and digital inequality. The proposed research paper will help to place AI as facilitative and not as a substitute to human instruction and provide some guidelines on how future research efforts should be performed in an inclusive, explainable, and ethically regulated AI-based learning system.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence in Personalised Education with the Development of Critical Thinking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 18:41:32","doi":"10.21203/rs.3.rs-8969990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"194868070546065877713675259548759692554","date":"2026-05-10T09:02:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T13:07:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286626734247033439966323026174632613517","date":"2026-04-22T13:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T20:19:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-19T15:30:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T08:43:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T08:53:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-03-12T04:50:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d051a506-fe0a-4a16-943f-18453aa058ff","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"194868070546065877713675259548759692554","date":"2026-05-10T09:02:32+00:00","index":99,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T18:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 18:41:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8969990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8969990","identity":"rs-8969990","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}